In recent years, channels and contents viewable by a television set are increased, such as terrestrial digital broadcast, BS digital broadcast, CS digital broadcast, pay-per-view (PPV) broadcast, video on demand (VOD), and contents on the Internet. Accordingly, operations required for a user to search for a desired program and to select a desired channel have become troublesome.
A storage capacity provided in a video recorder has been enlarged, and the video recorder has become capable of recording a massive amount of the programs. In order to find a program to be viewed from the massive programs recorded in the video recorder, the user (viewer) is required to use the functions, such as paging, sorting, and searching, which is a time-consuming and troublesome operation for the viewer.
As described above, the user interface of the video recorder has become more complicated as the number of channels and functions for the video recorder increases. Accordingly, there is an increasing need for a system learning a user's behavior and properly predicting the user's intention to appropriately operate.
There is known a system for digital television sets and video recorders, which learns a user's viewing behavior from a reproduction history of recorded contents and broadcast contents and recommends suitable contents to the user. For example, in JP-A-2006-229707, there is disclosed a system that accumulates a user's viewing behavior by storing days, times, genres of the reproduced recorded program and recommends to the user a program belonging to a genre that suits the day and time to be viewed.
A first problem that arises in the conventional system is that the learning of the user's viewing behavior requires a long period of time. In a system that learns a user's viewing behavior and recommending proper contents, the user may stop using such function if no remarkable merit is exerted as soon as the user starts using the system. Therefore, the learning of the user's viewing behavior is required to be made in a short period of time.
However, when the viewing behavior is learned by the combination of days and time zones, the learning requires quite a long period of time. For example, when a viewing behavior of a genre on a Sunday night is to be learned, the frequency of program genres viewed on Sunday nights is counted. Since the Sunday night comes four or five times a month, only several times of operation history could be acquired for a month by the use of such method. Accordingly, even when data is acquired for a month, the number of data for preparing a reliable viewing behavior model is not sufficient.
When a statistical learning method is used, the small number of frequency cannot guarantee the value in statistically correct probability. For example, when the night time zone is finely divided and discretized in the unit of, e.g., 1 hour to better understand the viewing behavior, the frequency is further dispersed. Accordingly, a longer-term learning is required to acquire reliable probability values.
A second problem that arises in the conventional system is a treatment of zapping operation, by which a program viewed by the viewer is frequently changed. The video recorder tends to be used in a manner where the zapping operation is less frequent for viewing the recorded programs, and thus information on the viewing time zone can be easily acquired. However, in a case where a television broadcast is directly viewed without being recorded, the viewer may tend to perform the zapping operation. When the user frequently switches the program to be viewed by the zapping operation, learning the user's viewing behavior becomes difficult. Accordingly, implementation of a method of applying a threshold value for the viewing time period is considered to exclude a channel operation determined as the zapping. For example, when the threshold value is set to be three minutes, a program to be viewed being changed in less than three minutes is considered to be zapped and is not considered as a viewed program.
However, when the threshold value is set for determining the zapping, all the programs viewed for the threshold value or less are excluded from the learning. That is, when the threshold value is set to, for example, three minutes and the user completes viewing a program having three minutes or less without the zapping operation, the system determines that the program was not viewed by the, whereby learning of a proper viewing behavior would not be performed.
A third problem that arises in the conventional system relates to the discretization of a viewing time period. In the statistical learning methods, attributes having a continuous value are often discretized. For example, since the attribute of time point has a continuous value, a method of dividing one day into 24 values in the unit of one hour such as 00:00 to 01:00 (0-1), 01:00 to 02:00 (1-2), and 02:00 to 03:00 (2-3) can be employed.
The viewing time point is used to learn the viewing behavior. For example, when a user views a movie from 21:00 to 22:00, the operation history data is assigned to 21-22 and the frequency is counted. However, when a user views a movie from 21:30 to 23:30, it is necessary to clarify how to discretize the viewing time point.